了解为scale_fill_continuous_divergingx输入的参数以处理颜色边距 [英] Understanding parameters inputting for scale_fill_continuous_divergingx for handling color margins
问题描述
此问题是我上一个问题的延续时，它建议您自定义参数比例来自colorspace包中的 divergingx_hcl 函数.
在查看 divergingx_hcl (v0.3.0)创建于2019-11-07 sup>
This question is a continuation of my previous question here.
I have a heatmap with a dataset available. The dataset is pasted below:
library(ggplot2)
library(colorspace)
bigtest <- structure(list(x = c(-8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8,
-8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, -8,
-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, -8, -7,
-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, -8, -7, -6,
-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, -8, -7, -6, -5,
-4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8),
y = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5),
z = c(1281.35043, 576.76381, 403.46607,
363.28815, 363.13356, 335.04997, 246.93314, 191.56371, 165.35087,
165.35087, 136.33712, 83.91203, 107.5773, 56.91087, 56.91089,
54.16559, 54.18172, 1841.60838, 1098.66304, 424.80686, 363.52776,
363.13355, 335.04998, 246.93314, 191.69473, 165.35087, 165.35087,
136.33712, 83.91204, 107.57729, 56.91087, 56.91088, 54.16421,
54.16794, 2012.52217, 1154.7927, 446.79023, 363.31379, 363.13356,
335.04997, 246.93314, 191.9613, 165.35087, 165.35087, 136.33712,
83.91202, 107.57731, 56.91088, 56.91088, 54.1642, 54.16559, 2077.10354,
1217.43403, 450.18301, 363.44225, 363.13357, 363.13363, 253.99753,
218.43223, 165.35087, 165.35014, 136.33712, 83.91203, 107.57822,
82.87399, 56.91087, 54.1642, 54.1642, 2092.56391, 1229.49925,
451.15179, 392.30728, 363.13356, 363.13282, 264.18944, 218.4308,
165.35087, 165.35044, 136.33712, 83.91202, 83.92709, 82.87353,
82.87406, 56.54491, 54.16421, 2206.93318, 1231.66411, 457.37767,
392.41558, 363.13357, 363.13283, 335.06272, 191.95211, 165.35087,
165.35014, 136.33712, 136.35211, 112.12755, 82.73634, 82.87353,
82.87418, 54.16421)),
row.names = c(NA, -102L),
class = c("tbl_df", "tbl", "data.frame"))
I'm generating a heatmap with the following code section:
ggplot(bigtest, aes(x = x, y = y)) +
geom_tile(aes(fill = z)) +
scale_fill_continuous_divergingx(palette = 'RdBu', rev = TRUE, mid = 347.48, l3 = 54, p3 = 2206, p4 = 325)
What I'm expecting from the plot is for the white color to be centered at a specific value and for the other gradients to diverge based on above or below that value. However, by working with the different parameters, it seems I don't fully understand what the parameters l3, p3, and p4 are referring to. When I was reviewing the documentation for this function it suggested that the parameters to customize the scale is from divergingx_hcl function within the colorspace package.
When reviewing the divergingx_hcl documentation it states they're coordinate corresponding to different input parameters. I'm completely lost and fully unaware of what this is. Any guidance on helping me wrap my head around these parameters (not just l3, p3, and p4 but the other parameters) would be greatly appreciated.
Created on 2019-11-07 by the reprex package (v0.3.0)
First, all colors are specified as HCL (hue, chroma, luminance), which correspond to the type of the color (red, green blue, etc.), how colorful a color is (low chroma is gray, high chroma is very colorful), and how light a color is (high luminance is white, low luminance is black).
The parameter l3 indicates the luminance component of the color at one end of the color scale. (l1 is the luminance at the other end, and l2 is the luminance in the middle.) Luminance goes from 0 to 100. So, if you want the color at the end to be darker, set luminance to a lower value. The parameters p3 and p4 are exponents that govern how quickly the colors transition from the midpoint to the endpoint. In general, values closer to 0 mean quicker transitions, and values greater than 1 mean slower transitions. It's unlikely you'll ever want p3 or p4 values greater than 10.
To get the default parameters for a palette, you can use the divergingx_palettes() command:
library(colorspace)
divergingx_palettes('RdBu')
#> HCL palette
#> Name: RdBu
#> Type: Diverging (flexible)
#> Parameter ranges:
#> h1 h2 h3 c1 c2 c3 l1 l2 l3 p1
#> 20 NA 230 60 0 50 20 98 15 1.4
Created on 2019-11-07 by the reprex package (v0.3.0)
This shows you that the color at the end point specified by l3 is already quite dark. Changing l3 from 15 to 0 will make it a bit darker but not by much. Further, p2, p3, and p4 are not specified, which means they're all taken from p1, and hence are 1.4. Thus, color interpolation is somewhat slower than linear.
With this knowledge, the following examples should make sense. To learn more about this, I recommend reading the various articles on the colorspace website: http://colorspace.r-forge.r-project.org/
First the data:
library(ggplot2)
library(colorspace)
bigtest <- structure(list(x = c(-8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8,
-8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, -8,
-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, -8, -7,
-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, -8, -7, -6,
-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, -8, -7, -6, -5,
-4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8),
y = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5),
z = c(1281.35043, 576.76381, 403.46607,
363.28815, 363.13356, 335.04997, 246.93314, 191.56371, 165.35087,
165.35087, 136.33712, 83.91203, 107.5773, 56.91087, 56.91089,
54.16559, 54.18172, 1841.60838, 1098.66304, 424.80686, 363.52776,
363.13355, 335.04998, 246.93314, 191.69473, 165.35087, 165.35087,
136.33712, 83.91204, 107.57729, 56.91087, 56.91088, 54.16421,
54.16794, 2012.52217, 1154.7927, 446.79023, 363.31379, 363.13356,
335.04997, 246.93314, 191.9613, 165.35087, 165.35087, 136.33712,
83.91202, 107.57731, 56.91088, 56.91088, 54.1642, 54.16559, 2077.10354,
1217.43403, 450.18301, 363.44225, 363.13357, 363.13363, 253.99753,
218.43223, 165.35087, 165.35014, 136.33712, 83.91203, 107.57822,
82.87399, 56.91087, 54.1642, 54.1642, 2092.56391, 1229.49925,
451.15179, 392.30728, 363.13356, 363.13282, 264.18944, 218.4308,
165.35087, 165.35044, 136.33712, 83.91202, 83.92709, 82.87353,
82.87406, 56.54491, 54.16421, 2206.93318, 1231.66411, 457.37767,
392.41558, 363.13357, 363.13283, 335.06272, 191.95211, 165.35087,
165.35014, 136.33712, 136.35211, 112.12755, 82.73634, 82.87353,
82.87418, 54.16421)),
row.names = c(NA, -102L),
class = c("tbl_df", "tbl", "data.frame"))
Now the plots:
ggplot(bigtest, aes(x = x, y = y)) +
geom_tile(aes(fill = z)) +
scale_fill_continuous_divergingx(
palette = 'RdBu', rev = TRUE,
mid = 347.48
)
ggplot(bigtest, aes(x = x, y = y)) +
geom_tile(aes(fill = z)) +
scale_fill_continuous_divergingx(
palette = 'RdBu', rev = TRUE,
mid = 347.48,
p3 = .2,
p4 = .2
)
ggplot(bigtest, aes(x = x, y = y)) +
geom_tile(aes(fill = z)) +
scale_fill_continuous_divergingx(
palette = 'RdBu', rev = TRUE,
mid = 347.48,
l3 = 0,
p3 = .2,
p4 = .2
)
Created on 2019-11-07 by the reprex package (v0.3.0)
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